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Meta interview notes, page 152 of 238

FAANG / Big Tech

Most of these Meta write-ups come from Software Engineers (1,194), then Data Scientists (489) and Product Managers (453). The most reported rounds are the technical phone screen (1,190), the product sense round (466), the system design round (255) and the behavioral / leadership round (205).

2377 interview notes · updated Jul 2026

Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a product design prompt around building a marketplace for celebrity engagement. Pretty open-ended, which sounds fun until you're actually in it.
  • Design a marketplace product that connects fans with celebrities for personalized engagement.

“I spent way too long on the user side and basically forgot to think about the supply side (the celebrities themselves) until I was halfway through.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Did a product analyst interview at Meta, one round, behavioral flavor with a data-focused twist. Nothing too wild but the question caught me a bit flat-footed.
  • Describe a project where you ran into significant data quality or availability problems. What did you do about it?

“I had a decent story but fumbled the structure a bit.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Meta ML engineer interview with two coding questions, both algorithmic but with a design flavor. The weighted sampling one was trickier than it looked and the sparse vector follow-up caught me a bit flat-footed.
  • Given a map of city names to their populations, implement a class that preprocesses the data and then lets you call a method repeatedly to randomly return a city, where the probability of picking a city is proportional to its population.
  • Compute the dot product of two sparse vectors, where each vector is given as a list of (index, value) pairs and the dimension can be enormous. Then, as a follow-up: if you're computing many dot products and only a few entries change between calls, how would you avoid recomputing from scratch each time?

“The core idea isn't hard: build a prefix sum array over sorted cities, generate a random number in [0, total_population), binary search for it.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta data engineer screen, just one coding question the whole time. Pretty focused on string manipulation and frequency counting, which I wasn't expecting to be the entire interview.
  • Given two strings, one original and one a partial or misspelled version, return the count of letters that need to be added to the second string so that some rearrangement of it could spell the first. Case-insensitive, multiplicities count.

“My first instinct was to sort both strings and diff them, which would've been fine but they nudged me toward O(n).”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta software engineering interview, two algorithmic problems back to back with a follow-up discussion tacked on at the end. Both problems were meaty enough that the session felt pretty compressed. Not a lot of small talk.
  • Given a permutation of integers, rearrange it in-place to produce the next permutation in lexicographic order. If none exists, wrap around to the smallest ordering. Aim for O(n) time and O(1) space, and walk through the algorithm and edge cases.
  • Implement a basic arithmetic expression evaluator for strings containing non-negative integers and the operators +, -, *, /. No parentheses, spaces may appear. Return an integer result using truncation toward zero for division. Target O(n) time and constant space.
  • Discussion only: how would you extend the evaluator to handle parentheses and correctly evaluate nested expressions with proper operator precedence?

“I knew the algorithm but explaining the 'why' behind each step while coding at the same time was harder than expected.”

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Meta·Data Scientist·Onsite - Product Sense / Strategy

Jun 2026
Meta DS interview focused on Facebook Groups, mixing product metrics with opportunity sizing. Pretty analytical throughout, felt more like a product sense round than a pure stats one.
  • What metrics would you use to define success for Facebook Groups?
  • How would you prioritize product improvements differently for large groups versus small groups?
  • Walk through an opportunity sizing exercise for a hypothetical local groups feature.

“I went straight to DAU/MAU and they let me talk for a bit before nudging me toward something more nuanced.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE interview with a debugging question around a maze renderer. Pretty focused, single problem, the kind of thing where you either see it fast or you spiral.
  • You're given a maze renderer that displays walls, open cells, a start, an end, and a computed path. A failing test shows the path symbol overwrites the start symbol. Find and fix the bug so the start symbol is always preserved, then verify with edge cases: path crosses the end cell, empty path, and start equals end.

“The core bug is a priority issue in the rendering logic, path symbols are applied after start/end symbols so they clobber them.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round with a classic array problem. Nothing too crazy but the edge cases will get you if you're not careful.
  • Given an array of house heights, return the indices of all houses that have an ocean view. A house has an ocean view if no house to its right is taller or equal in height.

“Went with a right-to-left scan, tracking the max height seen so far.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a product design question focused on building a hyperlocal feature for Facebook. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design a hyperlocal feature for Facebook?

“I went straight to neighborhoods and local events, which felt obvious in retrospect.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE interview with a tree traversal problem that sounds straightforward until you're actually in it. The twist of computing both left and right views in a single pass is what separates people who've thought about this from people who just memorized BFS.
  • Given the root of a binary search tree, return two lists: nodes visible from the left side and nodes visible from the right side, top to bottom. You must compute both views in a single traversal pass. Be ready to discuss iterative vs recursive approaches, how you track depth and first/last nodes at each level, time/space complexity, and edge cases like empty trees or duplicate keys.

“I started with BFS since tracking depth per level feels natural there, but then they pushed on doing it in one pass for both views simultaneously.”

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